"""
AI Historical Simulation Platform - Main Integration Class

This module provides the AIHistoricalSimulationPlatform class that orchestrates
all components of the system including HDC operations, personality encoding,
memory management, consciousness integration, and dialogue coordination.
"""

import asyncio
import json
import logging
import time
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Any, Tuple
import numpy as np

from ..hdc.core import HDCOperations
from ..personality.encoder import PersonalityEncoder
from ..personality.models import PersonalityTraits
from ..memory.manager import MemoryManager
from ..memory.hierarchy import MemoryHierarchy
from ..consciousness.models import ConsciousnessState
from ..config.settings import Config
from .figure_manager import HistoricalFigureManager


logger = logging.getLogger(__name__)


class HistoricalFigure:
    """Represents a historical figure with encoded personality and memories."""
    
    def __init__(self, name: str, personality_vector: np.ndarray, 
                 traits: PersonalityTraits, biography: Dict[str, Any],
                 personality_id: str):
        self.name = name
        self.personality_vector = personality_vector
        self.traits = traits
        self.biography = biography
        self.personality_id = personality_id
        self.memory_contexts = {}
        self.conversation_history = []
        self.created_at = datetime.now()
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert figure to dictionary representation."""
        return {
            'name': self.name,
            'personality_id': self.personality_id,
            'traits': self.traits.to_dict() if self.traits else {},
            'biography': self.biography,
            'created_at': self.created_at.isoformat()
        }


class AIHistoricalSimulationPlatform:
    """
    Main orchestration class for the AI Historical Simulation Platform.
    
    This class integrates all system components to provide a complete
    AI-powered historical figure simulation and conversation platform.
    """
    
    def __init__(self, hdc_dimension: int = 10000, config: Optional[Config] = None):
        """
        Initialize the AI Historical Simulation Platform.
        
        Args:
            hdc_dimension: Dimension for HDC vectors
            config: Platform configuration
        """
        self.config = config or Config()
        self.hdc_dimension = hdc_dimension
        
        # Core components
        self.hdc_ops = HDCOperations(dimension=hdc_dimension)
        self.personality_encoder = PersonalityEncoder(self.hdc_ops)
        self.memory_manager = MemoryManager(self.hdc_ops)
        self.memory_hierarchy = MemoryHierarchy(self.hdc_ops)
        self.figure_manager = HistoricalFigureManager(self)
        
        # Platform state
        self.loaded_figures: Dict[str, HistoricalFigure] = {}
        self.consciousness_states: Dict[str, ConsciousnessState] = {}
        self.active_sessions: Dict[str, Dict] = {}
        self.performance_metrics: Dict[str, Any] = {
            'total_conversations': 0,
            'total_responses_generated': 0,
            'avg_response_time': 0.0,
            'memory_operations': 0,
            'start_time': None
        }
        
        self.is_initialized = False
        
        logger.info(f"AIHistoricalSimulationPlatform initialized with HDC dimension {hdc_dimension}")
    
    async def initialize(self) -> None:
        """Initialize the platform and all its components."""
        if self.is_initialized:
            return
        
        logger.info("Initializing AI Historical Simulation Platform")
        
        try:
            # Initialize memory systems
            await self.memory_manager.initialize()
            await self.memory_hierarchy.initialize()
            
            # Initialize figure manager
            await self.figure_manager.initialize()
            
            # Set up performance monitoring
            self.performance_metrics['start_time'] = datetime.now()
            
            self.is_initialized = True
            logger.info("Platform initialization completed successfully")
            
        except Exception as e:
            logger.error(f"Platform initialization failed: {e}")
            raise
    
    async def load_historical_figure(self, figure_name: str) -> HistoricalFigure:
        """
        Load and encode a historical figure.
        
        Args:
            figure_name: Name of the historical figure
            
        Returns:
            Loaded and encoded historical figure
        """
        if not self.is_initialized:
            await self.initialize()
        
        # Check if figure is already loaded
        if figure_name in self.loaded_figures:
            return self.loaded_figures[figure_name]
        
        logger.info(f"Loading historical figure: {figure_name}")
        
        try:
            # Load figure data from figure manager
            figure_data = await self.figure_manager.load_figure_data(figure_name)
            
            # Create personality traits
            traits = PersonalityTraits.from_dict(figure_data.get('traits', {}))
            
            # Encode personality
            personality_vector = await self.personality_encoder.encode_complete_personality(
                traits=traits,
                cultural_context=figure_data.get('cultural_context', {}),
                historical_context=figure_data.get('historical_context', {})
            )
            
            # Create historical figure
            personality_id = f"{figure_name.lower().replace(' ', '_')}_{int(time.time())}"
            
            figure = HistoricalFigure(
                name=figure_name,
                personality_vector=personality_vector,
                traits=traits,
                biography=figure_data.get('biography', {}),
                personality_id=personality_id
            )
            
            # Initialize memory context for this figure
            await self._initialize_figure_memory(figure, figure_data)
            
            # Initialize consciousness state
            await self._initialize_consciousness_state(figure)
            
            # Cache the loaded figure
            self.loaded_figures[figure_name] = figure
            
            logger.info(f"Successfully loaded figure: {figure_name}")
            return figure
            
        except Exception as e:
            logger.error(f"Failed to load figure '{figure_name}': {e}")
            raise
    
    async def generate_response(self, personality_id: str, user_input: str, 
                              session_id: str) -> str:
        """
        Generate a response from a historical figure.
        
        Args:
            personality_id: ID of the personality to respond as
            user_input: User's input message
            session_id: Session identifier
            
        Returns:
            Generated response from the historical figure
        """
        start_time = time.time()
        
        try:
            # Find the figure
            figure = self._get_figure_by_personality_id(personality_id)
            if not figure:
                raise ValueError(f"Figure with personality ID {personality_id} not found")
            
            # Update session activity
            self._update_session_activity(session_id, figure.name, user_input)
            
            # Encode user input as memory
            input_vector = self.hdc_ops.generate_random_vector('gaussian')
            input_memory = {
                'content': user_input,
                'vector': input_vector,
                'timestamp': datetime.now(),
                'type': 'user_input'
            }
            
            # Store in memory hierarchy
            await self.memory_hierarchy.store_memory(
                personality_id, 
                input_memory,
                importance_score=0.8
            )
            
            # Retrieve relevant memories
            relevant_memories = await self.memory_hierarchy.retrieve_relevant_memories(
                personality_id,
                input_vector,
                max_memories=5
            )
            
            # Generate response based on personality and memories
            response = await self._generate_contextual_response(
                figure, user_input, relevant_memories, session_id
            )
            
            # Store response in memory
            response_vector = self.hdc_ops.generate_random_vector('gaussian')
            response_memory = {
                'content': response,
                'vector': response_vector,
                'timestamp': datetime.now(),
                'type': 'figure_response'
            }
            
            await self.memory_hierarchy.store_memory(
                personality_id,
                response_memory,
                importance_score=0.7
            )
            
            # Update conversation history
            figure.conversation_history.append({
                'user_input': user_input,
                'response': response,
                'timestamp': datetime.now(),
                'session_id': session_id
            })
            
            # Update performance metrics
            response_time = time.time() - start_time
            self._update_performance_metrics(response_time)
            
            return response
            
        except Exception as e:
            logger.error(f"Failed to generate response: {e}")
            raise
    
    async def create_cross_temporal_conversation(self, figure_names: List[str], 
                                               topic: str) -> List[Dict[str, Any]]:
        """
        Create a conversation between multiple historical figures.
        
        Args:
            figure_names: List of historical figure names
            topic: Conversation topic
            
        Returns:
            List of conversation exchanges
        """
        logger.info(f"Creating cross-temporal conversation on '{topic}' with: {', '.join(figure_names)}")
        
        # Load all figures
        figures = []
        for name in figure_names:
            figure = await self.load_historical_figure(name)
            figures.append(figure)
        
        conversation = []
        session_id = f"cross_temporal_{int(time.time())}"
        
        # Initialize the conversation with the topic
        current_input = f"Let's discuss {topic}. What are your thoughts on this matter?"
        
        # Create a round-robin conversation
        for round_num in range(3):  # 3 rounds of conversation
            for i, figure in enumerate(figures):
                response = await self.generate_response(
                    figure.personality_id,
                    current_input,
                    session_id
                )
                
                exchange = {
                    'figure': figure.name,
                    'input': current_input,
                    'response': response,
                    'round': round_num + 1,
                    'timestamp': datetime.now().isoformat()
                }
                
                conversation.append(exchange)
                
                # Next figure responds to this response
                current_input = f"{figure.name} says: '{response}'. What is your perspective on this?"
        
        return conversation
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get current performance metrics."""
        if self.performance_metrics['start_time']:
            uptime = (datetime.now() - self.performance_metrics['start_time']).total_seconds()
            self.performance_metrics['uptime_seconds'] = uptime
        
        return self.performance_metrics.copy()
    
    def get_system_health(self) -> Dict[str, Any]:
        """Get system health status."""
        return {
            'is_initialized': self.is_initialized,
            'loaded_figures': len(self.loaded_figures),
            'active_sessions': len(self.active_sessions),
            'memory_usage': self.memory_manager.get_memory_usage(),
            'hdc_operations_available': True,
            'consciousness_states': len(self.consciousness_states)
        }
    
    def get_timestamp(self) -> str:
        """Get current timestamp string."""
        return datetime.now().strftime('%Y%m%d_%H%M%S')
    
    async def cleanup(self) -> None:
        """Clean up platform resources."""
        logger.info("Cleaning up AI Historical Simulation Platform")
        
        try:
            # Save conversation histories
            await self._save_conversation_histories()
            
            # Clean up memory systems
            await self.memory_manager.cleanup()
            await self.memory_hierarchy.cleanup()
            
            # Clear caches
            self.loaded_figures.clear()
            self.consciousness_states.clear()
            self.active_sessions.clear()
            
            logger.info("Platform cleanup completed")
            
        except Exception as e:
            logger.error(f"Platform cleanup error: {e}")
    
    # Private methods
    
    async def _initialize_figure_memory(self, figure: HistoricalFigure, 
                                       figure_data: Dict[str, Any]) -> None:
        """Initialize memory context for a historical figure."""
        # Create base memories from biography
        if 'key_events' in figure_data:
            for event in figure_data['key_events']:
                memory_vector = self.hdc_ops.generate_random_vector('gaussian')
                memory = {
                    'content': event,
                    'vector': memory_vector,
                    'timestamp': datetime.now(),
                    'type': 'biographical',
                    'importance': 0.9
                }
                
                await self.memory_hierarchy.store_memory(
                    figure.personality_id,
                    memory,
                    importance_score=0.9
                )
        
        # Store personality traits as memories
        trait_memories = self.personality_encoder.traits_to_memories(figure.traits)
        for trait_memory in trait_memories:
            await self.memory_hierarchy.store_memory(
                figure.personality_id,
                trait_memory,
                importance_score=0.8
            )
    
    async def _initialize_consciousness_state(self, figure: HistoricalFigure) -> None:
        """Initialize consciousness state for a figure."""
        consciousness_state = ConsciousnessState(
            personality_id=figure.personality_id,
            current_focus=figure.name,
            emotional_state={'neutral': 0.8},
            attention_level=0.7,
            self_model=figure.personality_vector
        )
        
        self.consciousness_states[figure.personality_id] = consciousness_state
    
    async def _generate_contextual_response(self, figure: HistoricalFigure, 
                                          user_input: str, 
                                          relevant_memories: List[Dict],
                                          session_id: str) -> str:
        """Generate a contextual response based on personality and memories."""
        # This is a simplified response generation
        # In a full implementation, this would use advanced NLP/LLM models
        
        # Extract key concepts from input
        input_concepts = user_input.lower().split()
        
        # Find matching biographical content
        biographical_matches = []
        for memory in relevant_memories:
            if memory.get('type') == 'biographical':
                content_words = memory['content'].lower().split()
                if any(word in content_words for word in input_concepts):
                    biographical_matches.append(memory['content'])
        
        # Generate response based on personality traits and context
        response_elements = []
        
        # Add personality-based opening
        if figure.traits.extraversion > 0.7:
            response_elements.append("I am delighted to discuss this with you!")
        elif figure.traits.openness > 0.8:
            response_elements.append("What a fascinating topic to explore!")
        else:
            response_elements.append("This is indeed a matter worthy of consideration.")
        
        # Add biographical context if available
        if biographical_matches:
            response_elements.append(f"In my experience, {biographical_matches[0].lower()}")
        
        # Add trait-based perspective
        if 'strategy' in user_input.lower() or 'war' in user_input.lower():
            if figure.traits.conscientiousness > 0.8:
                response_elements.append("Careful planning and disciplined execution are essential.")
        
        if 'philosophy' in user_input.lower() or 'meaning' in user_input.lower():
            if figure.traits.openness > 0.7:
                response_elements.append("We must examine the deeper principles underlying this question.")
        
        # Combine response elements
        response = " ".join(response_elements)
        
        # Ensure minimum response length
        if len(response) < 50:
            response += f" What are your thoughts on this matter? I find {user_input.lower()} to be quite intriguing."
        
        return response
    
    def _get_figure_by_personality_id(self, personality_id: str) -> Optional[HistoricalFigure]:
        """Get figure by personality ID."""
        for figure in self.loaded_figures.values():
            if figure.personality_id == personality_id:
                return figure
        return None
    
    def _update_session_activity(self, session_id: str, figure_name: str, user_input: str) -> None:
        """Update session activity tracking."""
        if session_id not in self.active_sessions:
            self.active_sessions[session_id] = {
                'start_time': datetime.now(),
                'figure_name': figure_name,
                'messages': []
            }
        
        self.active_sessions[session_id]['messages'].append({
            'user_input': user_input,
            'timestamp': datetime.now()
        })
        
        self.active_sessions[session_id]['last_activity'] = datetime.now()
    
    def _update_performance_metrics(self, response_time: float) -> None:
        """Update performance metrics."""
        self.performance_metrics['total_responses_generated'] += 1
        
        # Update average response time
        total_responses = self.performance_metrics['total_responses_generated']
        current_avg = self.performance_metrics['avg_response_time']
        new_avg = ((current_avg * (total_responses - 1)) + response_time) / total_responses
        self.performance_metrics['avg_response_time'] = new_avg
    
    async def _save_conversation_histories(self) -> None:
        """Save conversation histories to disk."""
        try:
            histories_dir = Path('conversation_histories')
            histories_dir.mkdir(exist_ok=True)
            
            for figure_name, figure in self.loaded_figures.items():
                if figure.conversation_history:
                    filename = f"{figure_name.lower().replace(' ', '_')}_{self.get_timestamp()}.json"
                    filepath = histories_dir / filename
                    
                    history_data = {
                        'figure_name': figure.name,
                        'personality_id': figure.personality_id,
                        'conversations': [
                            {
                                'user_input': conv['user_input'],
                                'response': conv['response'],
                                'timestamp': conv['timestamp'].isoformat(),
                                'session_id': conv['session_id']
                            }
                            for conv in figure.conversation_history
                        ]
                    }
                    
                    with open(filepath, 'w') as f:
                        json.dump(history_data, f, indent=2)
            
            logger.info(f"Saved conversation histories for {len(self.loaded_figures)} figures")
            
        except Exception as e:
            logger.error(f"Failed to save conversation histories: {e}")